【正文】 Data mining for customer service support Abstract In traditional customer service support of a manufacturing environment, a customer service database usually stores two types of service information: unstructured customer service reports record machine problems and its remedial actions and structured data on sales, employees, and customers for daytoday management operations. This paper investigates how to apply data mining techniques to extract knowledge from the database to support two kinds of customer service activities: decision support and machine fault diagnosis. A data mining process, based on the data mining tool Database Miner, was investigated to provide structured management data for decision support. In addition, a data mining technique that integrates neural work, casebased reasoning, and rulebased reasoning is proposed。 it would search the unstructured customer service records for machine fault diagnosis. The proposed technique has been implemented to support intelligent fault diagnosis over the World Wide Web. Author Keywords: Data mining。 Knowledge discovery in databases。 Customer service support。 Decision support。 Machine fault diagnosis Introduction Customer service support is being an integral part of most multinational manufacturing panies that manufacture and market expensive machines and electronic equipment. Many panies have a customer service department that provides installation, inspection, and maintenance support for their worldwide customers. Although most of these have some engineers to handle daytoday maintenance and smallscale troubleshooting, expert advice is often required from the manufacturing panies for more plex maintenance and repair jobs. Prompt response to a request is needed to maintain customer satisfaction. Therefore, a hotline service centre (or help desk) is usually set up to answer frequently encountered problems from the customers. Fig. 1 shows the workout in a traditional hotline service centre. The service centre is responsible for receiving reports on faulty machines or enquiries from customers via telephone calls. When a problem is reported, a service engineer will suggest a series of checkpoints for customers using the hotline advisory system. Such suggestions are based on past experience. This has been extracted from a Customer Service Database, which contains previous service records that are identical or similar to the current problem. The customer can then try to solve the problem and subsequently confirm, with the service centre, if the problem is resolved. If the problem still persists, the centre will dispatch a service engineer to the customer39。s premise for an onsite repair. During such trips, the service engineer will take past records of the customer39。s machine, related manuals, and spare parts that may be required to carry out the repair. Such a process is inconvenient. At the end of each service cycle, a customer service report is used to record the new problem and the proposed remedies or suggestions taken to rectify it. This database is used for billing purposes, as well as for maintaining a corporate knowledge base. The service centre stores the customer service report in the database. Apart from maintaining a knowledge base on mon faults and its remedies, the customer service database also stores data on sales, employees, customers and service reports. These data are not only used for daytoday management operations, but help the pany in decision making on job assignment and promotion of service engineers, and marketing, manufacturing, and maintenance of different machine models. The customer service database serves as a repository of invaluable information and knowledge that can be utilized to assist the customer service department in supporting its activities. The objective of this paper is to discuss how to apply data mining techniques to extract knowledge from the customer service database to support two types of activities: decision support and machine fault diagnosis. The work was carried out as a collaborative work between a multinational pany and the School of Applied Science, Nan yang Technological University, Singapore. The pany manufactures and supplies insertion and surface mount machines for use mainly in the electronics industry. In traditional help desk service centres, service engineers provide a worldwide customer support service through the use of longdistance telephone calls. Such a mode of support is found to be inefficient, ineffective and generally results in high costs, long service cycles, and poor quality of service. With the advent of the Inter technology, it is possible to deli